Refinement of image quality in panoramic radiography using a generative adversarial network

Abstract

Objective: We aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography.

Methods: Panoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion (n = 100). Datasets with degraded image quality (n = 400) were prepared using four different processing methods: blur, noise, blur with noise, and blur in the anterior teeth region. The images were distributed to the training and test datasets in a ratio of 9:1 for each group. The Pix2Pix GAN model was trained using pairs of the original and degraded image datasets for 100 epochs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were obtained for the test dataset, and two oral and maxillofacial radiologists rated the quality of clinical images.

Results: Among the degraded images, the GAN model enabled the greatest improvement in those with blur in the region of the anterior teeth but was least effective in improving images exhibiting blur with noise (PSNR, 36.27 > 32.74; SSIM, 0.90 > 0.82). While the mean clinical image quality score of the original radiographs was 44.6 out of 46.0, the highest and lowest predicted scores were observed in the blur (45.2) and noise (36.0) groups. Conclusion: The GAN model developed in this study has the potential to improve panoramic radiographs with degraded image quality, both quantitatively and qualitatively. As the model performs better in refining blurred images, further research is required to identify the most effective methods for handling noisy images.